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Title: Predictive Coding and Internal Error Correction in Speech Production
Abstract

Speech production involves the careful orchestration of sophisticated systems, yet overt speech errors rarely occur under naturalistic conditions. The present functional magnetic resonance imaging study sought neural evidence for internal error detection and correction by leveraging a tongue twister paradigm that induces the potential for speech errors while excluding any overt errors from analysis. Previous work using the same paradigm in the context of silently articulated and imagined speech production tasks has demonstrated forward predictive signals in auditory cortex during speech and presented suggestive evidence of internal error correction in left posterior middle temporal gyrus (pMTG) on the basis that this area tended toward showing a stronger response when potential speech errors are biased toward nonwords compared to words (Okada et al., 2018). The present study built on this prior work by attempting to replicate the forward prediction and lexicality effects in nearly twice as many participants but introduced novel stimuli designed to further tax internal error correction and detection mechanisms by biasing speech errors toward taboo words. The forward prediction effect was replicated. While no evidence was found for a significant difference in brain response as a function of lexical status of the potential speech error, biasing potential errors toward taboo words elicited significantly greater response in left pMTG than biasing errors toward (neutral) words. Other brain areas showed preferential response for taboo words as well but responded below baseline and were less likely to reflect language processing as indicated by a decoding analysis, implicating left pMTG in internal error correction.

 
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NSF-PAR ID:
10379420
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
DOI PREFIX: 10.1162
Date Published:
Journal Name:
Neurobiology of Language
Volume:
4
Issue:
1
ISSN:
2641-4368
Page Range / eLocation ID:
p. 81-119
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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